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BackgroundThe quality of neutron radiographic images is mainly evaluated by human visual system (HVS), but HVS cannot be used as a real-time auxiliary for optimization parameters of neutron imaging systems.PurposeThis study aims to evaluate the quality of neutron radiographic images by no-reference image quality assessment (NR-IQA) and provide an effective approach for the parameters optimization of neutron imaging systems.MethodsFirstly, the plain natural images distorted with different distortion levels and types were labelled with quality scores to construct an experimental dataset by the gradient magnitude similarity deviation (GMSD) method. Then, the residual network (ResNet) model was employed to evaluate the quality of neutron radiographic images without reference images. Finally, the goal of extracting features and assessing the quality of neutron radiographic images was achieved by training the ResNet.ResultsThe model performs well in the test set of the experimental dataset and the quality prediction of the two group authentic neutron radiographic images.ConclusionsThe proposed quality assessment method could be used for the quality prediction of neutron radiographic images.

From the perspective of principal-agent, the theory of financial acceleration holds that due to the defects of the financial market, external shocks will be amplified by the financial market and accelerate the transmission in the real economy, and the impact on small enterprises is greater than that on large enterprises. According to the theory of financial deceleration, the agency cost caused by financial friction is counter-cyclical, which restrains credit scale to some extent, prevents excessive debt, and thus alleviates external shocks. According to the empirical analysis of panel data of iron and steel enterprises and the existing empirical results of the real estate industry, it is found that there is no financial accelerator effect or financial reducer effect in the field of iron and steel enterprises. China’s financial accelerator is more focused on bubbly assets where growth is expected and continues to be overheated despite policy tightening. That would trigger a bigger debt crisis and greater economic volatility.

Minor enterprises have made an indelible contribution to economic development, while financing has always been a problem hindering the sustainable development of them. Under the influence of the epidemic, the business environment of minor enterprises has changed, and the financing demand has also changed. However, the degree and scope of assistance are limited. There is a contradiction between supply and demand in fund allocation. Based on this, this project puts forward policy suggestions by studying the changes of financing demand and fund supplier of minor enterprises in Wuhan in the post epidemic era, which will help to solve the financing difficulties and promote healthy and sustainable development of the national economy.

Background: BRAF non-V600 mutation occupies a relatively small but critical subset in colorectal cancer (CRC). However, little is known about the biological functions and impacts of BRAF class III mutation in CRC. Here, we aim to explore how D594A mutation impacts on biological behaviors and immune related signatures in murine CRC cells. Methods: BRAF V600E (class I), G469V (class II) and D594A (class III) mutant cell lines were established based on MC38 cells. The biological behaviors of cells were evaluated in respect of cell growth, cell proliferation, cell apoptosis, cell migration and invasion by the methods of colony-forming assay, CCK-8 assay, Annexin V/PI staining and transwell assay. The concentrations of soluble cytokines were detected by ELISA. The membrane expression of immuno-modulatory molecules and the pattern of tumor infiltrating lymphocyte were evaluated by flow cytometry. The molecular mechanism was explored by RNA sequencing. Immunohistochemistry (IHC) staining was used for the detection of CD8α in tumor tissues. qRT-PCR and western blot were performed to assess the mRNA and protein expression. Anti-PD-L1 treatment and cytokines neutralization experiments were conducted in in vivo models. Results: D594A mutant cells displayed lower grade malignancy characteristics than V600E (class I) and G469V (class II) mutant cells. Meanwhile, D594A mutation led to evident immuno-modulatory features including upregulation of MHC Class I and PD-L1. In vivo experiments displayed that the frequency of infiltrated CD8+ T cells was significantly high within D594A mutant tumors, which may provide potential response to anti-PD-L1 therapy. RNA sequencing analysis showed that D594A mutation led to enhanced expression of ATF3 and THBS1, which thus facilitated CXCL9 and CXCL10 production upon IFN-γ treatment. In addition, CXCL9 or CXCL10 neutralization reduced the infiltration of CD8+ T cells into THBS1-overexpressing tumors. Conclusions: D594A mutant CRC exhibited lower aggressiveness and immune-activated phenotype. ATF3-THBS1-CXCL9/CXCL10 axis mediated functional CD8+ T cells infiltration into the microenvironment of D594A mutant CRC. Our present study is helpful to define this mutation in CRC and provide important insights in designing effective immunotherapeutic strategies in clinic. [ABSTRACT FROM AUTHOR]

Leaf water content (LWC) is one of the important indicators of crop health. It plays an important role in the physiological process of leaves, participates in almost all physiological processes of crops, and is of great significance to the survival and growth of crops. Based on the hyperspectral (350–1350 nm) and LWC data (jointing, booting, flowering, and filling periods) of winter wheat in 2020 and 2021, this work proposed to transform and process the hyperspectral data by adopting fractional order differential and continuous wavelet transform, and took a differential spectrum, wavelet coefficients, and mixed variables (differential spectrum and wavelet coefficients) as input variables of the model and adopted Gaussian process regression (GPR), classification and regression decision tree (CART), and artificial neural network (ANN) methods to estimate the LWC of wheat in different growth periods. The results indicated that fractional differential and continuous wavelet transform could highlight the spectral characteristics of winter wheat canopy and improve its correlation with LWC. The three model variables had the best estimation effect on LWC in the flowering period, and the average values of R2 were 0.86 and 0.87 in modeling and verification, which indicated that the flowering period could be used as the best estimation period for LWC. Compared with the differential spectrum and wavelet coefficients, LWC estimation based on mixed variables performed best. The average values of R2 in modeling and verification were 0.78 and 0.79. Among them, the ANN model had the highest estimation accuracy, and the R2 in modeling and verification could reach 0.92 and 0.91. This showed that fractional differential and continuous wavelet transform could effectively promote the sensitivity of spectral information to LWC and enhance the prediction ability and stability of wheat LWC. The outcomes of the present study have the potential to provide new ideas for the water monitoring of crops. [ABSTRACT FROM AUTHOR]